Data mining refers in general to data-driven approaches for extracting hidden information from input data. The extracted information depends on a type of data mining and is put together in data mining models. This model information can be further analyzed or verified against further process information.
Data mining techniques typically need to consider how to effectively process large amounts of data. Consider manufacturing of products as an example. There, the input data can include various pieces of data relating to origin and features of components. The aim of data mining in the context of manufacturing can be to resolve problems relating to quality analysis and quality assurance. Data mining can be used, for example, for root cause analysis, for early warning systems within the manufacture plant, and for reducing warranty claims.
As another example, data mining can be used for intrusion detection, system monitoring, and problem analyses. Data mining has also various other uses, for example, in retail and services, where typical customer behavior can be analyzed, in medicine and life sciences for finding causal relations in clinical studies.